Out-of-Distribution Detection Algorithms for Robust Insect Classification.

Plants encounter a variety of beneficial and harmful insects during their growth cycle. Accurate identification (i.e., detecting insects' presence) and classification (i.e., determining the type or class) of these insect species is critical for implementing prompt and suitable mitigation strategies. Such timely actions carry substantial economic and environmental implications. Deep learning-based approaches have produced models with good insect classification accuracy. Researchers aim to implement identification and classification models in agriculture, facing challenges when input images markedly deviate from the training distribution (e.g., images like vehicles, humans, or a blurred image or insect class that is not yet trained on). Out-of-distribution (OOD) detection algorithms provide an exciting avenue to overcome these challenges as they ensure that a model abstains from making incorrect classification predictions on images that belong to non-insect and/or untrained insect classes. As far as we know, no prior in-depth exploration has been conducted on the role of the OOD detection algorithms in addressing agricultural issues. Here, we generate and evaluate the performance of state-of-the-art OOD algorithms on insect detection classifiers. These algorithms represent a diversity of methods for addressing an OOD problem. Specifically, we focus on extrusive algorithms, i.e., algorithms that wrap around a well-trained classifier without the need for additional co-training. We compared three OOD detection algorithms: (a) maximum softmax probability, which uses the softmax value as a confidence score; (b) Mahalanobis distance (MAH)-based algorithm, which uses a generative classification approach; and (c) energy-based algorithm, which maps the input data to a scalar value, called energy. We performed an extensive series of evaluations of these OOD algorithms across three performance axes: (a) Base model accuracy: How does the accuracy of the classifier impact OOD performance? (b) How does the level of dissimilarity to the domain impact OOD performance? (c) Data imbalance: How sensitive is OOD performance to the imbalance in per-class sample size? Evaluating OOD algorithms across these performance axes provides practical guidelines to ensure the robust performance of well-trained models in the wild, which is a key consideration for agricultural applications. Based on this analysis, we proposed the most effective OOD algorithm as wrapper for the insect classifier with highest accuracy. We presented the results of its OOD detection performance in the paper. Our results indicate that OOD detection algorithms can significantly enhance user trust in insect pest classification by abstaining classification under uncertain conditions.

Self-supervised maize kernel classification and segmentation for embryo identification.

Introduction: Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Methods: Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels. Results and discussion: We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

"Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.

Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as 'canopy fingerprints'. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.

Outcome of Intra-articular Injection of Total Stromal Cells and Platelet-Rich Plasma in Primary Knee Osteoarthritis: A Randomized Clinical Trial.

INTRODUCTION:  Mesenchymal stem cell (MSC) therapy appeared promising in knee osteoarthritis (OA). We examined if a single intra-articular (IA) autologous total stromal cells (TSC) and platelet-rich plasma (PRP) injection improved knee pain, physical function, and articular cartilage thickness in knee OA. METHODS:  The study was performed in the physical medicine and rehabilitation department of Bangabandhu Shaikh Mujib Medical University, Dhaka, Bangladesh. Knee OA was diagnosed according to the American College of Rheumatology criteria and randomly assigned to treatment (received TSC and PRP) and control groups. Kallgreen-Lawrance (KL) scoring system was used to grade primary knee OA. The Visual Analogue Scale (VAS, 0-10 cm) for pain, WOMAC (Western Ontario and McMaster Universities Arthritis Index) for physical function, and medial femoral condylar cartilage (MFC) thickness (millimeters) under ultrasonogram (US) were documented and compared between groups before and after treatment. Statistical Package analyzed data for Social Scientists (SPSS 22.0; IBM Corp, Armonk, NY) was used for data analysis. Pre- and post-intervention outcomes were measured using the Wilcoxon-sign test, whereas Mann-Whitney U-test calculated the difference between groups; a p-value <0.05 was considered statistically significant.  Result: In the treatment group, 15 received IA-TSC and PRP preparation, and in the control group, 15 patients received no injection, but quadricep muscle-strengthening exercise. There was no significant difference between groups regarding VAS for pain, WOMAC physical function, and cartilage thickness before starting the treatment and two weeks after intervention. VAS for pain and WOMAC physical function scores improved profoundly in the treatment group after 12 and 24 weeks of intervention; the pain and physical function scores difference between groups was also significant. However, significant mean femoral cartilage thickness was not changed until the end of 24 weeks (U=175.00, p=0.009 two-tailed and U= 130.00, p=0.016 two-tailed, respectively, for right and left knee). CONCLUSION:  Single TSC and PRP injection reduces knee pain and improves physical function and cartilage thickness in knee OA. While pain and physical function improvement happen earlier, cartilage thickness change takes more time.

Protocols for In Vivo Doubled Haploid (DH) Technology in Maize Breeding: From Haploid Inducer Development to Haploid Genome Doubling.

Doubled haploid (DH) technology reduces the time required to obtain homozygous genotypes and accelerates plant breeding among other advantages. It is established in major crop species such as wheat, barley, maize, and canola. DH lines can be produced by both in vitro and in vivo methods and the latter is focused here. The major steps involved in in vivo DH technology are haploid induction, haploid selection/identification, and haploid genome doubling. Herein, we elaborate on the various steps of DH technology in maize breeding from haploid induction to haploid genome doubling to produce DH lines. Detailed protocols on the following topics are discussed: in vivo haploid inducer line development, haploid selection using seed and root color markers and automated seed sorting based on embryo oil content using QSorter, artificial genome doubling, and the identification of genotypes with spontaneous haploid genome doubling (SHGD) ability.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP).

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N2) is fixed into bioavailable ammonia (NH3) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit (R 2 = 0.99). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.

Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel.

Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.

Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters.

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications = 14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

Computer vision and machine learning enabled soybean root phenotyping pipeline.

BACKGROUND: Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. RESULTS: This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. CONCLUSIONS: This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor.

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.

A farm-level precision land management framework based on integer programming.

Farmland management involves several planning and decision making tasks including seed selection and irrigation management. A farm-level precision farmland management model based on mixed integer linear programming is proposed in this study. Optimal decisions are designed for pre-season planning of crops and irrigation water allocation. The model captures the effect of size and shape of decision scale as well as special irrigation patterns. The authors illustrate the model with a case study on a farm in the state of California in the U.S. and show the model can capture the impact of precision farm management on profitability. The results show that threefold increase of annual net profit for farmers could be achieved by carefully choosing irrigation and seed selection. Although farmers could increase profits by applying precision management to seed or irrigation alone, profit increase is more significant if farmers apply precision management on seed and irrigation simultaneously. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture.

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity.

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use.

Dielectrophoretic separation of bioparticles in microdevices: a review.

In recent years, dielectrophoretic force has been used to manipulate colloids, inert particles, and biological microparticles, such as red blood cells, white blood cells, platelets, cancer cells, bacteria, yeast, microorganisms, proteins, DNA, etc. This specific electrokinetic technique has been used for trapping, sorting, focusing, filtration, patterning, assembly, and separating biological entities/particles suspended in a buffer medium. Dielectrophoretic forces acting on particles depend on various parameters, for example, charge of the particle, geometry of the device, dielectric constant of the medium and particle, and physiology of the particle. Therefore, to design an effective micro-/nanofluidic separation platform, it is necessary to understand the role of the aforementioned parameters on particle motion. In this paper, we review studies particularly related to dielectrophoretic separation in microfluidic devices. Both experimental and theoretical works by several researchers are highlighted in this article covering AC and DC DEP. In addition, AC/DC DEP, which uses a combination of low frequency AC and DC voltage to manipulate bioparticles, has been discussed briefly. Contactless DEP, a variation of DC DEP in which electrodes do not come in contact with particles, has also been reviewed. Moreover, dielectrophoretic force-based field flow fractionations are featured to demonstrate the bioparticle separation in microfluidic device. In numerical front, a comprehensive review is provided starting from the most simplified effective moment Stokes-drag (EMSD) method to the most advanced interface resolved method. Unlike EMSD method, recently developed advanced numerical methods consider the size and shape of the particle in the electric and flow field calculations, and these methods provide much more accurate results than the EMSD method for microparticles.

A new design for efficient dielectrophoretic separation of cells in a microdevice.

Effectiveness of a continuous biological cell separation device can be improved significantly by increasing the distance among different types of cells. To achieve this, most of the recent dielectrophoresis based continuous separation devices implement differential forces on cells either along the transverse direction or the vertical direction with respect to the bulk fluid flow motion. However, interparticle distance can be increased further by implementing forces along both transverse and vertical planes. In this article, a design for a microfluidic platform has been proposed where a new electrode configuration is identified to implement symmetric forces in both vertical and transverse directions. A numerical model, which considers presence of particles in the electric field and flow field, shows a much higher interparticle distance between red blood cells and plasmodium falciparum infected red blood cells in such a device than that in a conventional separation device. This configuration also reduces the possibility of particle trapping on the electrodes, which is a major bottleneck of dielectrophoresis.

A new fabrication technique to form complex polymethylmethacrylate microchannel for bioseparation.

Recent studies show that reduction in cross-sectional area can be used to improve the concentration factor in microscale bioseparations. Due to simplicity in fabrication process, a step reduction in cross-sectional area is generally implemented in microchip to increase the concentration factor. But the sudden change in cross-sectional area can introduce significant band dispersion and distortion. This paper reports a new fabrication technique to form a gradual reduction in cross-sectional area in polymethylmethacrylate (PMMA) microchannel for both anionic and cationic isotachophoresis (ITP). The fabrication technique is based on hot embossing and surface modification assisted bonding method. Both one-dimensional and two-dimensional gradual reduction in cross-sectional area microchannels were formed on PMMA with high fidelity using proposed techniques. ITP experiments were conducted to separate and preconcentrate fluorescent proteins in these microchips. Thousand fold and ten thousand fold increase in concentrations were obtained when 10 × and 100 × gradual reduction in cross-sectional area microchannels were used for ITP.

Modeling and simulation of nanoparticle separation through a solid-state nanopore.

Recent experimental studies show that electrokinetic phenomena such as electroosmosis and electrophoresis can be used to separate nanoparticles on the basis of their size and charge using nanopore-based devices. However, the efficient separation through a nanopore depends on a number of factors such as externally applied voltage, size and charge density of particle, size and charge density of membrane pore, and the concentration of bulk electrolyte. To design an efficient nanopore-based separation platform, a continuum-based mathematical model is used for fluid. The model is based on Poisson-Nernst-Planck equations along with Navier-Stokes equations for fluid flow and on the Langevin equation for particle translocation. Our numerical study reveals that membrane pore surface charge density is a vital parameter in the separation through a nanopore. In this study, we have simulated high-density lipoprotein (HDL) and low-density lipoprotein (LDL) as the sample nanoparticles to demonstrate the capability of such a platform. Numerical results suggest that efficient separation of HDL from LDL in a 0.2 M KCL solution (resembling blood buffer) through a 150 nm pore is possible if the pore surface charge density is ∼ -4.0 mC/m(2). Moreover, we observe that pore length and diameter are relatively less important in the nanoparticle separation process considered here.

10,000-fold concentration increase of the biomarker cardiac troponin I in a reducing union microfluidic chip using cationic isotachophoresis.

This paper describes the preconcentration of the biomarker cardiac troponin I (cTnI) and a fluorescent protein (R-phycoerythrin) using cationic isotachophoresis (ITP) in a 3.9 cm long poly(methyl methacrylate) (PMMA) microfluidic chip. The microfluidic chip includes a channel with a 5× reduction in depth and a 10× reduction in width. Thus, the overall cross-sectional area decreases by 50× from inlet (anode) to outlet (cathode). The concentration is inversely proportional to the cross-sectional area so that as proteins migrate through the reductions, the concentrations increase proportionally. In addition, the proteins gain additional concentration by ITP. We observe that by performing ITP in a cross-sectional area reducing microfluidic chip we can attain concentration factors greater than 10,000. The starting concentration of cTnI was 2.3 µg mL⁻¹ and the final concentration after ITP concentration in the microfluidic chip was 25.52 ± 1.25 mg mL⁻¹. To the author's knowledge this is the first attempt at concentrating the cardiac biomarker cTnI by ITP. This experimental approach could be coupled to an immunoassay based technique and has the potential to lower limits of detection, increase sensitivity, and quantify different isolated cTnI phosphorylation states.

10,000-fold concentration increase in proteins in a cascade microchip using anionic ITP by a 3-D numerical simulation with experimental results.

This paper describes both the experimental application and 3-D numerical simulation of isotachophoresis (ITP) in a 3.2 cm long "cascade" poly(methyl methacrylate) (PMMA) microfluidic chip. The microchip includes 10 × reductions in both the width and depth of the microchannel, which decreases the overall cross-sectional area by a factor of 100 between the inlet (cathode) and outlet (anode). A 3-D numerical simulation of ITP is outlined and is a first example of an ITP simulation in three dimensions. The 3-D numerical simulation uses COMSOL Multiphysics v4.0a to concentrate two generic proteins and monitor protein migration through the microchannel. In performing an ITP simulation on this microchip platform, we observe an increase in concentration by over a factor of more than 10,000 due to the combination of ITP stacking and the reduction in cross-sectional area. Two fluorescent proteins, green fluorescent protein and R-phycoerythrin, were used to experimentally visualize ITP through the fabricated microfluidic chip. The initial concentration of each protein in the sample was 1.995 µg/mL and, after preconcentration by ITP, the final concentrations of the two fluorescent proteins were 32.57 ± 3.63 and 22.81 ± 4.61 mg/mL, respectively. Thus, experimentally the two fluorescent proteins were concentrated by over a factor of 10,000 and show good qualitative agreement with our simulation results.

Chemically modified solid state nanopores for high throughput nanoparticle separation.

The separation of biomolecules and other nanoparticles is a vital step in several analytical and diagnostic techniques. Towards this end we present a solid state nanopore-based set-up as an efficient separation platform. The translocation of charged particles through a nanopore was first modeled mathematically using the multi-ion model and the surface charge density of the nanopore membrane was identified as a critical parameter that determines the selectivity of the membrane and the throughput of the separation process. Drawing from these simulations a single 150 nm pore was fabricated in a 50 nm thick free-standing silicon nitride membrane by focused-ion-beam milling and was chemically modified with (3-aminopropyl)triethoxysilane to change its surface charge density. This chemically modified membrane was then used to separate 22 and 58 nm polystyrene nanoparticles in solution. Once optimized, this approach can readily be scaled up to nanopore arrays which would function as a key component of next-generation nanosieving systems.